Pathogen Surveillance Report

Summary

This report is produced by the nf-core/pathogensurveillance pipeline.

  • Report group: no_group_defined
  • Sample count: 3
  • Last updated: May 30 , 2025
  • Pipeline version: dev

Pipeline status

The pipeline finished execution. Warning or error messages describing problems encountered in the analysis might be provided below.

No issues reported.

A list of issues reported by the pipeline during execution. When relevant, the sample IDs or reference IDs associated with the issue are included.

Input data

Identification

Initial identification

The following data provides tentative classifications of the samples based on exact matches of a subset of short DNA sequences. These are intended to be preliminary identifications. For more robust identifications based on whole genome sequences, see “Phylogenetic context” section below.

Initial classification of 3 samples identified all of them as:

sk:Bacteria > p:Proteobacteria > c:Gammaproteobacteria > o:Enterobacterales > f:Enterobacteriaceae > g:Klebsiella > s:Klebsiella pneumoniae

This table shows the “highest scoring” tentative taxonomic classification for each sample. Included metrics can provide insights into how each sample compares with reference genomes on online databases and the likelihood these comparisons are valid.

  • Sample: The sample ID submitted by the user.
  • WKID: Weighted k-mer Identity, adjusted for genome size differences.
  • ANI: An estimate of average nucleotide identity (ANI), derived from WKID and kmer length.
  • Completeness: The percentage of the reference genome represented in the query.
  • Top Hit: The name of the reference genome most similar to each sample based on the scoring criteria used.

Most similar organisms

This table shows the Average Nucleotide Identity (ANI) between each sample and the two references most similar to it based on this measure. ANI is used to measure how similar the shared portion of two genomes are. Note that this measure only takes into account the shared portion of genomes, so differences like extra plasmids or chromosomal duplications are not taken into account.

This plot shows the results of comparing the similarity of all samples and references to each other. These similarity metrics are based on the presence and abundance of short exact sequence matches between samples (i.e. comparisons of k-mer sketches). These measurements are not as reliable as the methods used to create phylogenetic trees, but may be useful if phylogenetic trees could not be inferred for these samples.

This table shows the Percentage Of Conserved Proteins (POCP) between each sample and the 2 references most similar to it based on this measure. POCP is used to measure the proportion of proteins shared between two genomes. Which proteins are shared is determined from pairwise comparisons of all proteins between all genomes. The POCP between two genomes is the sum of the number of shared proteins in each genome divided by the sum of the number of total proteins in each genome (Qin et al. 2014). Currently, POCP is only calculated for Prokaryotes.

This plot shows the results of comparing the protein content of all samples and references to each other. POCP is used to measure the proportion of proteins shared between two genomes. Which proteins are shared is determined from pairwise comparisons of all proteins between all genomes. The POCP between two genomes is the sum of the number of shared proteins in each genome divided by the sum of the number of total proteins in each genome (Qin et al. 2014). Currently, POCP is only calculated for Prokaryotes.

Phylogenetic context

Shown are phylogenetic trees of samples with references sequences downloaded from RefSeq meant to provide a reliable identification using genome-scale data. The accuracy of this identification depends on the presence of close reference sequences in RefSeq and the accuracy of the initial identification.

Multigene phylogeny

Color By:

This a multigene phylogeny of samples with reference genomes for context. It is the most robust identification provided by this pipeline, but taxonomic coverage is still limited by the availability of similar reference sequences.

Genetic diversity

SNP trees

Color:

This is a representation of a Single Nucleotide Polymorphism (SNP) tree, depicting the genetic relationships among samples in comparison to a reference assembly.

The tree is less robust than a core gene phylogeny and cannot offer insights on evolutionary relationships among strains, but it does offer one way to visualize the genetic diversity among samples, with genetically similar strains clustering together.

Question-does it make sense to be showing the reference within the tree?

Minimum spanning network

Threshold:

This figure depicts a minimium spanning network (MSN). The nodes represent unique multiocus genotypes, and the size of nodes is proportional to the # number of samples that share the same genotype.

The edges represent the SNP differences between two given genotypes, and the darker the color of the edges, the fewer SNP differences between the two.

Note: within these MSNs, edge lengths are not proportional to SNP differences.

Software and references

Methods

The pathogen surveillance pipeline used the following tools that should be referenced as appropriate:

  • A sample is first identified to genus using sendsketch and further identified to species using sourmash (Brown and Irber 2016).
  • The nextflow data-driven computational pipeline enables deployment of complex parallel and reactive workflows (Di Tommaso et al. 2017).

Analysis software

module program version citation
ASSIGN_CORE_REFERENCES r-base 4.2.1 R Core Team (2021)
ASSIGN_MAPPING_REFERENCE r-base 4.2.1 R Core Team (2021)
BAKTA_BAKTA bakta 1.10.4 Schwengers et al. (2021)
BAKTA_BAKTADBDOWNLOAD bakta 1.10.4 Schwengers et al. (2021)
BUSCO_DOWNLOAD busco 5.8.2 Manni et al. (2021)
BWA_INDEX bwa 0.7.18-r1243-dirty Li and Durbin (2009)
BWA_MEM bwa 0.7.18-r1243-dirty Li and Durbin (2009)
BWA_MEM samtools 1.2 Danecek et al. (2021)
CALCULATE_POCP r-base 4.2.1 R Core Team (2021)
EXTRACT_FEATURE_SEQUENCES pirate 1.0.5 Bayliss et al. (2019)
FASTP fastp 0.23.4 Chen (2023)
FASTQC fastqc 0.12.1 Andrews et al. (2010)
FILTER_ASSEMBLY biopython 1.78 NA
GATK4_VARIANTFILTRATION gatk4 4.6.1.0 Van der Auwera and O’Connor (2020)
GRAPHTYPER_GENOTYPE graphtyper 2.7.7 Eggertsson et al. (2017)
GRAPHTYPER_VCFCONCATENATE graphtyper 2.7.7 Eggertsson et al. (2017)
IQTREE_CORE iqtree 2.4.0 Nguyen et al. (2015)
IQTREE_SNP iqtree 2.4.0 Nguyen et al. (2015)
MAFFT_CORE mafft 7.52 Katoh et al. (2002)
MAFFT_CORE pigz 2.8) NA
MAKE_GFF_WITH_FASTA sed 4.7 NA
PICARD_CREATESEQUENCEDICTIONARY picard 3.3.0 “Picard Toolkit” (2019)
PICARD_FORMAT picard 3.1.1 “Picard Toolkit” (2019)
PIRATE pirate 1.0.5 Bayliss et al. (2019)
QUAST quast 5.3.0 Mikheenko et al. (2018)
REFORMAT_PIRATE_RESULTS pirate 1.0.5 Bayliss et al. (2019)
SAMPLESHEET_CHECK r-base 4.4.3 R Core Team (2021)
SAMTOOLS_FAIDX samtools 1.21 Danecek et al. (2021)
SAMTOOLS_INDEX samtools 1.21 Danecek et al. (2021)
SOURMASH_COMPARE sourmash 4.8.14 Brown and Irber (2016)
SOURMASH_SKETCH sourmash 4.8.14 Brown and Irber (2016)
SPADES spades 4.0.0 Prjibelski et al. (2020)
SUBSET_CORE_GENES r-base 4.2.1 R Core Team (2021)
TABIX_BGZIP tabix 1.2 Li (2011)
TABIX_TABIX tabix 1.2 Li (2011)
VCFLIB_VCFFILTER vcflib 1.0.3 Garrison et al. (2022)
VCF_TO_SNP_ALIGN r-base 4.2.1 R Core Team (2021)
Workflow nf-core/pathogensurveillance v1.0.0
Workflow Nextflow 24.10.0 Di Tommaso et al. (2017)

R packages used

R version 4.4.3 (2025-02-28)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] PathoSurveilR_0.3.1

loaded via a namespace (and not attached):
  [1] mnormt_2.1.1            gridExtra_2.3           polysat_1.7-7          
  [4] phangorn_2.12.1         permute_0.9-7           rlang_1.1.5            
  [7] magrittr_2.0.3          ade4_1.7-23             compiler_4.4.3         
 [10] mgcv_1.9-1              vctrs_0.6.5             maps_3.4.2.1           
 [13] reshape2_1.4.4          combinat_0.0-8          quadprog_1.5-8         
 [16] stringr_1.5.1           pkgconfig_2.0.3         fastmap_1.2.0          
 [19] labeling_0.4.3          ca_0.71.1               promises_1.3.2         
 [22] rmarkdown_2.29          purrr_1.0.4             xfun_0.51              
 [25] cachem_1.1.0            seqinr_4.2-36           aplot_0.2.5            
 [28] clusterGeneration_1.3.8 jsonlite_1.9.1          later_1.4.1            
 [31] adegenet_2.1.11         cluster_2.1.8           parallel_4.4.3         
 [34] R6_2.6.1                bslib_0.9.0             stringi_1.8.4          
 [37] RColorBrewer_1.1-3      boot_1.3-31             jquerylib_0.1.4        
 [40] numDeriv_2016.8-1.1     Rcpp_1.0.14             assertthat_0.2.1       
 [43] iterators_1.0.14        knitr_1.50              optimParallel_1.0-2    
 [46] base64enc_0.1-3         splines_4.4.3           httpuv_1.6.15          
 [49] Matrix_1.7-2            igraph_2.1.4            tidyselect_1.2.1       
 [52] yaml_2.3.10             viridis_0.6.5           vegan_2.6-10           
 [55] TSP_1.2-4               doParallel_1.0.17       codetools_0.2-20       
 [58] lattice_0.22-6          tibble_3.2.1            plyr_1.8.9             
 [61] shiny_1.10.0            treeio_1.30.0           withr_3.0.2            
 [64] coda_0.19-4.1           evaluate_1.0.3          phytools_2.4-4         
 [67] gridGraphics_0.5-1      heatmaply_1.5.0         pillar_1.10.1          
 [70] ggtree_3.14.0           DT_0.33                 foreach_1.5.2          
 [73] ggfun_0.1.8             plotly_4.10.4           generics_0.1.3         
 [76] ggplot2_3.5.1           munsell_0.5.1           scales_1.3.0           
 [79] tidytree_0.4.6          xtable_1.8-4            bspm_0.5.7             
 [82] glue_1.8.0              scatterplot3d_0.3-44    lazyeval_0.2.2         
 [85] tools_4.4.3             dendextend_1.19.0       ggnewscale_0.5.1       
 [88] data.table_1.17.0       webshot_0.5.5           registry_0.5-1         
 [91] fs_1.6.5                fastmatch_1.1-6         grid_4.4.3             
 [94] tidyr_1.3.1             ape_5.8-1               crosstalk_1.2.1        
 [97] seriation_1.5.7         colorspace_2.1-1        nlme_3.1-167           
[100] patchwork_1.3.0         cli_3.6.4               DEoptim_2.2-8          
[103] expm_1.0-0              viridisLite_0.4.2       poppr_2.9.6            
[106] dplyr_1.1.4             gtable_0.3.6            yulab.utils_0.2.0      
[109] sass_0.4.9              digest_0.6.37           ggplotify_0.1.2        
[112] htmlwidgets_1.6.4       farver_2.1.2            htmltools_0.5.8.1      
[115] lifecycle_1.0.4         pegas_1.3               httr_1.4.7             
[118] mime_0.13               MASS_7.3-65            

References

Andrews, Simon et al. 2010. “FastQC: A Quality Control Tool for High Throughput Sequence Data.” Cambridge, United Kingdom.
Bayliss, Sion C, Harry A Thorpe, Nicola M Coyle, Samuel K Sheppard, and Edward J Feil. 2019. “PIRATE: A Fast and Scalable Pangenomics Toolbox for Clustering Diverged Orthologues in Bacteria.” Gigascience 8 (10): giz119.
Brown, C Titus, and Luiz Irber. 2016. “Sourmash: A Library for MinHash Sketching of DNA.” Journal of Open Source Software 1 (5): 27.
Chen, Shifu. 2023. “Ultrafast One-Pass FASTQ Data Preprocessing, Quality Control, and Deduplication Using Fastp.” Imeta 2 (2): e107.
Danecek, Petr, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, et al. 2021. “Twelve Years of SAMtools and BCFtools.” Gigascience 10 (2): giab008.
Di Tommaso, Paolo, Maria Chatzou, Evan W Floden, Pablo Prieto Barja, Emilio Palumbo, and Cedric Notredame. 2017. “Nextflow Enables Reproducible Computational Workflows.” Nature Biotechnology 35 (4): 316–19.
Distribution, Anaconda Software. 2016. “Computer Software.” Vers. 4: 2–2.
Eggertsson, Hannes P, Hakon Jonsson, Snaedis Kristmundsdottir, Eirikur Hjartarson, Birte Kehr, Gisli Masson, Florian Zink, et al. 2017. “Graphtyper Enables Population-Scale Genotyping Using Pangenome Graphs.” Nature Genetics 49 (11): 1654–60.
Garrison, Erik, Zev N Kronenberg, Eric T Dawson, Brent S Pedersen, and Pjotr Prins. 2022. “A Spectrum of Free Software Tools for Processing the VCF Variant Call Format: Vcflib, Bio-Vcf, Cyvcf2, Hts-Nim and Slivar.” PLoS Computational Biology 18 (5): e1009123.
Katoh, Kazutaka, Kazuharu Misawa, Kei-ichi Kuma, and Takashi Miyata. 2002. “MAFFT: A Novel Method for Rapid Multiple Sequence Alignment Based on Fast Fourier Transform.” Nucleic Acids Research 30 (14): 3059–66.
Kurtzer, Gregory M, Vanessa Sochat, and Michael W Bauer. 2017. “Singularity: Scientific Containers for Mobility of Compute.” PloS One 12 (5): e0177459.
Li, Heng. 2011. “Tabix: Fast Retrieval of Sequence Features from Generic TAB-Delimited Files.” Bioinformatics 27 (5): 718–19.
Li, Heng, and Richard Durbin. 2009. “Fast and Accurate Short Read Alignment with Burrows–Wheeler Transform.” Bioinformatics 25 (14): 1754–60.
Manni, Mosè, Matthew R Berkeley, Mathieu Seppey, Felipe A Simão, and Evgeny M Zdobnov. 2021. “BUSCO Update: Novel and Streamlined Workflows Along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes.” Molecular Biology and Evolution 38 (10): 4647–54.
Mikheenko, Alla, Andrey Prjibelski, Vladislav Saveliev, Dmitry Antipov, and Alexey Gurevich. 2018. “Versatile Genome Assembly Evaluation with QUAST-LG.” Bioinformatics 34 (13): i142–50.
Nguyen, Lam-Tung, Heiko A Schmidt, Arndt Von Haeseler, and Bui Quang Minh. 2015. “IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies.” Molecular Biology and Evolution 32 (1): 268–74.
“Picard Toolkit.” 2019. Broad Institute, GitHub Repository. https://broadinstitute.github.io/picard/; Broad Institute.
Prjibelski, Andrey, Dmitry Antipov, Dmitry Meleshko, Alla Lapidus, and Anton Korobeynikov. 2020. “Using SPAdes de Novo Assembler.” Current Protocols in Bioinformatics 70 (1): e102.
Qin, Qi-Long, Bin-Bin Xie, Xi-Ying Zhang, Xiu-Lan Chen, Bai-Cheng Zhou, Jizhong Zhou, Aharon Oren, and Yu-Zhong Zhang. 2014. “A Proposed Genus Boundary for the Prokaryotes Based on Genomic Insights.” Journal of Bacteriology 196 (12): 2210–15.
R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Schwengers, Oliver, Lukas Jelonek, Marius Alfred Dieckmann, Sebastian Beyvers, Jochen Blom, and Alexander Goesmann. 2021. “Bakta: Rapid and Standardized Annotation of Bacterial Genomes via Alignment-Free Sequence Identification.” Microbial Genomics 7 (11): 000685.
Van der Auwera, Geraldine A, and Brian D O’Connor. 2020. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O’Reilly Media.

About

Contributors

The nf-core/pathogen surveillance pipeline was developed by: Zach Foster, Martha Sudermann, Camilo Parada-Rojas, Logan Blair, Fernanda Iruegas-Bocardo, Ricardo Alcalá-Briseño, Alexandra Weisberg, Jeff Chang and Nik Grünwald.

Funding

This pipeline is supported by NIFA grants 2021-67021-34433, 2023-67013-39918 to JHC and NJG and ARS Project 2072-22000-045-000-D to NJG.

Contribute

To contribute, provide feedback, or report bugs please visit our github repository.

Citations

Please cite this pipeline and nf-core in publications as follows:

Foster et al. 2025. PathogenSurveillance: A nf-core pipeline for rapid analysis of pathogen genome data. In preparation.

Di Tommaso, Paolo, Maria Chatzou, Evan W Floden, Pablo Prieto Barja, Emilio Palumbo, and Cedric Notredame. 2017. Nextflow Enables Reproducible Computational Workflows. Nature Biotechnology 35: 316–19. https://doi.org/10.1038/nbt.3820.

Other tools

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